Quantitative equity management with modern data infrastructure
Los Angeles Capital Management runs a proprietary quantitative stock-selection model across a data stack anchored in Snowflake, Python, and Databricks. Active adoption of dbt, Airflow, Prefect, and MLflow alongside Terraform-managed cloud infrastructure signals a shift toward automated, governed data pipelines—a necessary move given their stated pain points around reliability, cost, and manual process overhead. Hiring velocity is accelerating across engineering, data, and research roles, matching the complexity of scaling a quantitative platform.
Founded in 2002, LACM is an independent investment manager headquartered in Los Angeles focused on quantitative equity portfolios powered by its proprietary Dynamic Alpha Stock Selection Model. The firm operates across 51–200 employees with a lean, specialized structure: finance, compliance, marketing, legal, research, engineering, and data functions. Current project work centers on modernizing their data foundation—building pipelines for predictive models, dashboards for model inputs, data catalogs, and governance frameworks—while managing compliance and infrastructure automation in the cloud.
Primary stack: Snowflake, Python, NumPy, scikit-learn, SQL, Databricks. Currently adopting dbt, Apache Airflow, Prefect, MLflow, Tableau, and Terraform for data engineering and infrastructure automation.
Primarily United States. Secondary hiring activity in Peru noted in geographic footprint.
Other companies in the same industry, closest in size